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Created page with "{{Dimensions |nd = 1 |nx = 3 |nw = 2 }} The '''Moon Landing problem''' is a simplification of a spacecraft trying to land on the moon's surface. Its objective is to minimize the fuel consumption during the landing maneuver while landing savely on the ground with zero vertical velocity. The implementation here is taken from [1]. Its dynamics are given by a two-dimensional ODE model. == Mathematical formulatio..."
 
 
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<math>
<math>
  \begin{array}{lll}
  \begin{array}{lll}
  \displaystyle \min_{u} && -m(t_f) \\
  \displaystyle \min_{T, t_\mathrm{f}} && -m(t_\mathrm{f}) \\
  \text{subject to} \\
  \text{subject to} \\
\quad \dot{h}(t) & = & v(t),\\
\quad \dot{h}(t) & = & v(t),\\
\quad \dot{v}(t) & = & -1 + \frac{T(t)}{m}, \\
\quad \dot{v}(t) & = & -1 + \frac{T(t)}{m(t)}, \\
\quad \dot{m}(t) & = & -\frac{T(t)}{2.349}, \\
\quad \dot{m}(t) & = & -\frac{T(t)}{2.349}, \\
\quad h(0) &=& 1, \\
\quad h(0) &=& 1, \\
\quad v(0) &=& -0.783, \\
\quad v(0) &=& -0.783, \\
\quad m(0) &=& 1, \\
\quad m(0) &=& 1, \\
\quad h(t_f) &=& 0, \\
\quad t_\mathrm{f} &\geq& 0, \\
\quad v(t_f) &=& 0, \\
\quad h(t_\mathrm{f}) &=& 0, \\
\quad T(t) & \in & [0, 1.227] \ \quad \forall t \in [0,t_f]
\quad v(t_\mathrm{f}) &=& 0, \\
\quad T(t) & \in & [0, 1.227] \ \quad \forall t \in [0,t_\mathrm{f}]
   \end{array}
   \end{array}
</math>
</math>
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<gallery caption="Reference solution plots" widths="500px" heights="300px" perrow="1">
<gallery caption="Reference solution plots" widths="500px" heights="300px" perrow="1">
  Image:Moon_Landing.png| States and discretized control for a local optimum.
  Image:Moon_Landing.png| States and discretized control for a local optimum. The free end time <math>t_\mathrm{f}</math> was modeled using the additional control <math>t</math>.
</gallery>
</gallery>


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== References ==
== References ==
<span id="openmdao">[1]</span> Multidisciplinary Optimal Control Library: https://openmdao.org/dymos/docs/latest/examples/moon_landing/moon_landing.html<br>
<span id="openmdao">[1]</span> Multidisciplinary Optimal Control Library: https://openmdao.org/dymos/docs/latest/examples/moon_landing/moon_landing.html
<span id="Moo90">[2]</span> Andrew William Moore. Efficient memory-based learning for robot control. Technical Report UCAM-CL-TR-209, University of Cambridge, Computer Laboratory, November 1990. URL: https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-209.pdf, doi:10.48456/tr-209.<br>
<span id="MMB14">[3]</span> Alexey A Melnikov, Adi Makmal, and Hans J Briegel. Projective simulation applied to the grid-world and the mountain-car problem. arXiv preprint arXiv:1405.5459, 2014.<br>
 
[[Category:MIOCP]]
[[Category:MIOCP]]
[[Category:Bang bang]]
[[Category:Bang bang]]

Latest revision as of 17:25, 22 February 2026

Moon Landing
State dimension: 1
Differential states: 3
Discrete control functions: 2


The Moon Landing problem is a simplification of a spacecraft trying to land on the moon's surface. Its objective is to minimize the fuel consumption during the landing maneuver while landing savely on the ground with zero vertical velocity.

The implementation here is taken from [1]. Its dynamics are given by a two-dimensional ODE model.

Mathematical formulation

minT,tfm(tf)subject toh˙(t)=v(t),v˙(t)=1+T(t)m(t),m˙(t)=T(t)2.349,h(0)=1,v(0)=0.783,m(0)=1,tf0,h(tf)=0,v(tf)=0,T(t)[0,1.227] t[0,tf]

Reference Solutions

Here is one local solution to the above control problem.

Miscellaneous and Further Reading

This formulation and a detailed description can be found in [1].

References

[1] Multidisciplinary Optimal Control Library: https://openmdao.org/dymos/docs/latest/examples/moon_landing/moon_landing.html